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PARS-Mitteilungen 2017

https://dl.gi.de/handle/20.500.12116/31834
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Auflistung PARS-Mitteilungen 2017 nach Autor:in "Bromberger, Michael"

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  • Zeitschriftenartikel
    Design Space Exploration Including Approximate Computing for OpenCL-based Stereo Vision Hardware
    (PARS-Mitteilungen: Vol. 34, Nr. 1, 2017) Bromberger, Michael; Ehrle, Steffen; Scharrer, Michael; Erlinghagen Lukas; Schick, Jens
    Calculating distances from objects to a subject, for instance a car, is a central task in many applications. Such distances can be calculated by stereo vision exploiting stereo camera images. The high complexity of this approach, which has to be performed under high-performance and lowpower constraints, limits a wide usage. Hardware acceleration is a promising solution to meet above constraints. Two main approaches exist, local ones work on a pixel-wise scheme and global ones consider all pixels at the same time, which highly increases the memory and time complexity. Several optimization methods exist to find Pareto-optimal designs in the design space spanned by accuracy, performance, and resource consumption. Besides well-known techniques, we design, implement, and evaluate new methods, which includes the current research trend of approximate computing. Therefore, in this paper we evaluate different optimization techniques on an OpenCL level for local as well as semi-global approaches. While we target on resource reduction for local approaches, we tackle the memory issue of semi-global approaches. We implement all methods on a low-power and low-cost FPGA-based system on chip and evaluate them on available benchmarks as well as on a real-world scenario. The novel semi-global approximate computing design provides a high frame rate, supports a high number of disparities, and achieves a good accuracy on typical traffic scenes.
  • Zeitschriftenartikel
    Evaluating the Influence of Data Type Precision On Numerical Algorithms
    (PARS-Mitteilungen: Vol. 34, Nr. 1, 2017) Bromberger, Michael; Hoffmann, Markus; Hampp, Andreas Hampp
    IEEE 32 or 64 bit floating-point arithmetic is often sufficient for different kind of algorithms including scientific applications. However, there is a growing body of applications which have significant computational errors during the calculation leading to incorrect results. Such applications are ranging from numerical algorithms and probabilistic timing analysis to long-time simulations. While designing numerically stable algorithms or interval arithmetic pose possible solutions for certain problems, most scientific programmers are not aware of such deep numerical analyses. In addition, not all issues can be solved using above methods. High precision arithmetic, which is provided by software libraries or coprocessor designs, is a promising solution to overcome above numerical issues. Therefore, we investigate the influence of data type precision on a numerical algorithm, i.e. Lanczos algorithm, and compare different high precision arithmetic software libraries regarding accuracy and execution time. Additionally, we examine the usage of an exact scalar product for the Lanczos algorithm. While we show that high precision arithmetic is crucial for numerical algorithms, such arithmetic is still by far slower than hardware-supported data types.